Standardisation of Temperature Observed by Automatic Weather Stations

Daily mean, maximum and minimum surface airtemperature data were gathered from a network ofautomatic weather stations (AWS) within the Moor HouseNational Nature Reserve in northern England. Five AWSwere installed next to the official EnvironmentalChange Network weather station at Moor House. Datawere compared graphically and correction constantswere calculated to adjust data from each AWS to thestandard of the official station by optimising theconcordance correlation coefficient. Each correctedstation was re-located next to one of five in-situstations in and around the reserve, allowingcorrection of all temperature sensors to a commonstandard. The mean error associated with measureddaily mean, maximum and minimum temperature for eachsensor does not exceed ±0.2 K. The procedurequantifies a source of systematic measurement error,improving the identification of spatial temperaturedifferences between stations.     

Heterogeneity of Thermal Extremes: Driven by Disturbance or Inherent in the Landscape

Ecologists are beginning to recognize the effect of heterogeneity on structure and function in arid and semiarid ecosystems. Additionally, the influences of temperature on ecosystems are widely documented, but landscape temperature patterns and relationships with vegetation are rarely reported in ecological studies. To better understand the importance of temperature patterns to the conservation and restoration of native ecosystems, we designed an experiment to investigate relationships among soil surface temperature, landscape heterogeneity, and grazing intensity. Grazing intensity did influence the vegetation structure and composition. Heavy treatments had the greatest bare ground and the least vertical structure. Ungrazed treatments had the most litter and live grass cover. However, average temperatures among the three grazing treatments were not different and ranged less than 2°C during midday summer periods. The temperature difference between riparian and upland landscapes within grazing treatments was 21°C. Landscape position (riparian vs. upland) did have a significant influence on soil surface temperature and produced a variation in temperature 11 times greater than grazing intensities. Thermal heterogeneity did not differ among grazing treatments. Lower soil surface temperatures (associated with riparian areas) may provide a critical thermal refuge for many animals in arid and semiarid ecosystems on hot summer days, when air temperatures can exceed 37°C. Riparian zones, specifically riparian vegetation, are an important component in ecosystem management.     

中温焙烧/钠化氧化法回收电镀污泥中的铬

采用中温焙烧/钠化氧化法从电镀污泥中回收铬.结果表明,影响铬浸出率的最主要因素为焙烧温度.电镀污泥与碳酸钠质量比、焙烧时间、水浸时间对铬浸出率的影响较接近,在水浸水固比为10.0 ; 1.0(质量比)、室温、焙烧温度为650℃、焙烧时间为2.0h、电镀污泥与碳酸钠质量比为1:1、水浸时间为60 min的最佳浸出条件下,铬浸出率为99.3%;去除氢氧化铝、氢氧化锌的最佳反应温度和pH分别为90～95℃和7.5;去除硫酸钠晶体的最佳pH为4.0,在最佳试验条件下,铬回收率为90.57%.     

试论低碳经济背景下的政府职能

伴随着经济与社会的进步,能源短缺与环境污染正愈演愈烈,迫切需要由高碳经济转向低碳经济。中国目前的发展模式与消费结构仍处于高碳状态,因此发展低碳经济势在必行。政府作为经济活动的有机组成部分,应当在发展低碳经济的过程中履行好其对外交流、对内调控的政府职能。     

基于T-P模型的高低温环境型煤对甲烷吸附性能预测

为预测深部或浅部煤层不同温度和不同压力条件下的吸附等温线，选用型煤以高低温试验装置为依托，测试了温度为293.15，273.15，253.15 K的吸附等温线。基于T-P模型，利用等温吸附曲线对公式中的参数进行了合理的求解，探讨了一种简单的煤对瓦斯吸附等温线预测方法。研究表明:同一吸附平衡压力下，温度越低，煤的瓦斯吸附量越大；ε－ω吸附特征曲线与温度无关，呈现对数的形式；参数m和拟合度R2满足抛物线的关系，存在拟合效果最好时的参数m值。采用T-P模型预测得到的吸附等温线与实测的吸附等温线无论是趋势还是定量结果均十分吻合，其相对误差不超过5%。     

淋雨及不同温度条件对消防员防护服各部位热阻的影响

为了研究淋雨及不同温度条件对消防员防护服各部位热阻的影响，以消防防护服为研究对象，利用环境舱和暖体假人系统，在20 mm/h降雨强度和20~30 ℃的复合环境条件下，研究淋雨、不同温度复合环境对服装各部位热阻的影响。研究结果表明:淋雨过程中，服装热阻的变化趋势为先上升再下降最后趋于稳定；淋雨与不同温度复合环境会使得消防防护服热阻降低，其中影响较大的部位为上臂部、腹部、胸部、背部及臀部等。     

低水温条件下CAST工艺在城市污水处理厂的应用

本文主要介绍了CAST工艺应用于污水处理厂的工艺流程和相关特点,在低温条件下的运行成果分析,并且探讨了设计和运行过程当中的各种问题,目的就是为北方CAST工艺的城市污水处理厂,在冬季低进水温度的条件下的应用,提供相关的处理措施。     

改性活性炭脱除硫化氢工艺参数研究

针对改性活性炭脱除硫化氢过程,研究了空速、温度、原料气浓度、颗粒分布孔径4个主要工艺参数对脱硫效率的影响。结果表明:空速在1 500～4 200h-1时穿透硫容随着空速的降低而增加,当空速继续降低为1 200h-1时穿透硫容基本不变;当0～40℃时,随着温度的升高,穿透时间增加,脱硫效率提高,当温度超过40℃时,随着温度继续升高脱硫效率降低;相同空速下原料气硫化氢浓度变化只改变穿透时间;改性活性炭脱硫剂发挥脱硫作用的微孔结构范围是1～5nm。     

Starting improvement of micro-wind turbines operating in low wind speed regions

The study deals with the design and optimization of external and internal geometry of micro-wind turbines blades. A specified objective function which consists of the power coefficient and the starting time was defined and the genetic algorithm optimization technique in conjunction with the blade-element momentum theory was adopted to find the geometry of the blades including the distributions of the chord, the twist angle and also the shell thickness. Moreover, the allowable stress of the blades was considered as a constraint to the objective function. Results show that a reasonable compromise is achievable such that the starting time of the blades reduces noticeably in return for a small drop in the power coefficient. The significant improvement of the hollow blades over the solid ones indicates that the power coefficient and the starting performance could be improved through the appropriate distributions of the considered decision variables, i.e. the chord, the twist angle and also the shell thickness.     

Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

The volcanic plate made pillar cooler system is designed for outdoor spaces as a heat exchanging medium and reduces the outcoming air temperature which flows through the exhaust port. This paper proposes the use of artificial neural networks (ANNs) to predict inside air temperature of a pillar cooler. For this purpose, at first, three statistically significant factors (outside temperature, airing and watering) influencing the inside air temperature of the pillar cooler are identified as input parameters for predicting the output (inside air temperature) and then an ANN was employed to predict the output. In addition, 70%, 15% and 15% data was chosen from a previously obtained data set during the field trial of the pillar cooler for training, testing and validation, respectively, and then an ANN was employed to predict inside air temperature. The training (0.99918), testing (0.99799) and validation errors (0.99432) obtained from the model indicate that the artificial neural network model (NARX) may be used to predict inside air temperature of pillar cooler. This study reveals that, an ANN approach can be used successfully for predicting the performance of pillar cooler.